In radiotherapy, the goal is typically to deliver a sufficiently high radiation dose to a target (for example a tumor) within the patient, while sparing surrounding normal tissue as far as possible. In particular, it is important to minimize the dose to sensitive organs close to the target. A treatment plan, defining treatment parameters, such as treatment machine settings, to be used in a radiotherapy treatment session, is usually determined with the aid of a computer-based treatment planning system (TPS). In inverse treatment planning, an optimization algorithm is employed for finding a set of treatment parameters that will generate an acceptable dose distribution within the patient, preferably satisfying all the clinical goals defined by the clinician.
Radiotherapy treatment planning is based on medical images, such as three-dimensional CT images. In order to serve as basis for treatment planning, these images must be segmented. Segmentation of an image refers to the process of defining or reconstructing different internal structures or other regions of interest (ROIs) in an image. In the field of radiotherapy, these structures could for example be specific internal organs, or target volumes (such as tumors), which are identifiable in the images.
Preferably, any radiotherapy treatment plan, regardless of the method used to produce the plan, should be robust, meaning that the plan should efficiently take any uncertainties into consideration. Sources of uncertainty include for example uncertainty in the position of the target relative to the beams, uncertainty regarding the location of cancer cells, and uncertainty in the patient density data. Hence, a robust treatment plan must be insensitive to any errors occurring due to such uncertainties.
Traditionally, uncertainties have often been handled by applying margins to ROIs. As a result, treatment planning is based on enlarged volumes, ensuring adequate dose coverage of targets and/or sufficient sparing of risk organs. For example, a margin applied to a Clinical Target Volume (CTV) defines the Planning Target Volume (PTV). In order to limit the dose to healthy tissue as much as possible, the PTV should not be larger than necessary.
Treatment planning using margins is usually considered to be adequate in the field of conventional photon-based radiotherapy within regions of relatively homogeneous density. This is because the “static dose cloud approximation” can be considered to be valid, meaning that a deformation of the patient anatomy has negligible impact on the dose distribution in space. Hence, the patient can be assumed to be moving within a static dose distribution, such that, for example, the effect of a patient setup error corresponds to a rigid translation of the dose distribution. However, the static dose cloud approximation is usually not valid in regions of less homogeneous density, and is usually not suitable for particle radiotherapy, for example using protons or other ions, since the Bragg peak positions (and thus the resulting dose distribution) are highly dependent on the densities of the traversed tissues. The effectiveness of margin-based treatment planning in these situations might therefore be questioned. Nonetheless, the use of margins is still heavily relied upon.
Alternative methods based on a probabilistic approach for achieving robust radiation treatment plans have been proposed, wherein uncertainties are taken into account explicitly in the optimization by considering the (usually unknown) probability distributions of the uncertain parameters when optimizing a treatment plan. In order to yield a computational optimization problem, the possible realizations of the uncertainties are often discretized into a plurality of scenarios, where each scenario corresponds to a specific realization of the uncertainties. As a simple example, different scenarios can be defined by different rigid translations of the patient, corresponding to different possible setup errors. Using an approach based on expected value optimization, the expectation of an objective value over the scenario doses is minimized. Using worst case scenario optimization (“minimax” optimization), the objective function in the worst case scenario (the scenario resulting in the least favorable dose distribution) is minimized. Such, and other, optimization techniques for robust treatment planning are well-known in the art and are discussed for example by A. Fredriksson in “A characterization of robust radiation therapy treatment planning methods—from expected value to worst case optimization” (Med. Phys., 39 (8):5169-5181, 2012). While such methods can produce robust plans also for sites of heterogeneous density, they result in qualitatively different plans than does margin-based planning, even in situations where the static dose cloud approximation holds. For example, using expected value optimization might result in blunt dose fall-offs, i.e. blurred dose distributions, compared to the sharp dose fall-off obtained with margin-based treatment planning. It is generally preferred to have a steep dose fall-off since a blunt fall-off will increase the integral dose to the patient without substantially increasing the probability of tumor control. Using worst case optimization might be too conservative in many situations.
While many methods according to the prior art might result in fairly robust treatment plans in specific cases, there is still much room for improvement, and none of the known methods are, in a satisfying way, universally applicable. For example, methods yielding good results for a “simple” case in a region of relatively homogeneous density might not result in sufficiently robust treatment plans for a complicated case in regions with heterogeneous density (e.g. a lung tumor case). Correspondingly, a method which is suitable for a complicated case might be too conservative for a simpler case, resulting in a suboptimal treatment plan.
An aim of the present invention is to overcome, or at least mitigate, the drawbacks described above, and in particular to provide a treatment planning system that will enable robust treatment plans to be generated for a plurality of different clinical cases and different treatment modalities.